Quantum Entanglement of a Harmonic Oscillator with an Electromagnetic Field
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Theory and Application of Fermi Pseudo-Potential in One Dimension
Theory and application of Fermi pseudo-potential in one dimension The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Wu, Tai Tsun, and Ming Lun Yu. 2002. “Theory and Application of Fermi Pseudo-Potential in One Dimension.” Journal of Mathematical Physics 43 (12): 5949–76. https:// doi.org/10.1063/1.1519940. Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:41555821 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA CERN-TH/2002-097 Theory and application of Fermi pseudo-potential in one dimension Tai Tsun Wu Gordon McKay Laboratory, Harvard University, Cambridge, Massachusetts, U.S.A., and Theoretical Physics Division, CERN, Geneva, Switzerland and Ming Lun Yu 41019 Pajaro Drive, Fremont, California, U.S.A.∗ Abstract The theory of interaction at one point is developed for the one-dimensional Schr¨odinger equation. In analog with the three-dimensional case, the resulting interaction is referred to as the Fermi pseudo-potential. The dominant feature of this one-dimensional problem comes from the fact that the real line becomes disconnected when one point is removed. The general interaction at one point is found to be the sum of three terms, the well-known delta-function potential arXiv:math-ph/0208030v1 21 Aug 2002 and two Fermi pseudo-potentials, one odd under space reflection and the other even. -
Simulating Quantum Field Theory with a Quantum Computer
Simulating quantum field theory with a quantum computer John Preskill Lattice 2018 28 July 2018 This talk has two parts (1) Near-term prospects for quantum computing. (2) Opportunities in quantum simulation of quantum field theory. Exascale digital computers will advance our knowledge of QCD, but some challenges will remain, especially concerning real-time evolution and properties of nuclear matter and quark-gluon plasma at nonzero temperature and chemical potential. Digital computers may never be able to address these (and other) problems; quantum computers will solve them eventually, though I’m not sure when. The physics payoff may still be far away, but today’s research can hasten the arrival of a new era in which quantum simulation fuels progress in fundamental physics. Frontiers of Physics short distance long distance complexity Higgs boson Large scale structure “More is different” Neutrino masses Cosmic microwave Many-body entanglement background Supersymmetry Phases of quantum Dark matter matter Quantum gravity Dark energy Quantum computing String theory Gravitational waves Quantum spacetime particle collision molecular chemistry entangled electrons A quantum computer can simulate efficiently any physical process that occurs in Nature. (Maybe. We don’t actually know for sure.) superconductor black hole early universe Two fundamental ideas (1) Quantum complexity Why we think quantum computing is powerful. (2) Quantum error correction Why we think quantum computing is scalable. A complete description of a typical quantum state of just 300 qubits requires more bits than the number of atoms in the visible universe. Why we think quantum computing is powerful We know examples of problems that can be solved efficiently by a quantum computer, where we believe the problems are hard for classical computers. -
Quantum Machine Learning: Benefits and Practical Examples
Quantum Machine Learning: Benefits and Practical Examples Frank Phillipson1[0000-0003-4580-7521] 1 TNO, Anna van Buerenplein 1, 2595 DA Den Haag, The Netherlands [email protected] Abstract. A quantum computer that is useful in practice, is expected to be devel- oped in the next few years. An important application is expected to be machine learning, where benefits are expected on run time, capacity and learning effi- ciency. In this paper, these benefits are presented and for each benefit an example application is presented. A quantum hybrid Helmholtz machine use quantum sampling to improve run time, a quantum Hopfield neural network shows an im- proved capacity and a variational quantum circuit based neural network is ex- pected to deliver a higher learning efficiency. Keywords: Quantum Machine Learning, Quantum Computing, Near Future Quantum Applications. 1 Introduction Quantum computers make use of quantum-mechanical phenomena, such as superposi- tion and entanglement, to perform operations on data [1]. Where classical computers require the data to be encoded into binary digits (bits), each of which is always in one of two definite states (0 or 1), quantum computation uses quantum bits, which can be in superpositions of states. These computers would theoretically be able to solve certain problems much more quickly than any classical computer that use even the best cur- rently known algorithms. Examples are integer factorization using Shor's algorithm or the simulation of quantum many-body systems. This benefit is also called ‘quantum supremacy’ [2], which only recently has been claimed for the first time [3]. There are two different quantum computing paradigms. -
Many Physicists Believe That Entanglement Is The
NEWS FEATURE SPACE. TIME. ENTANGLEMENT. n early 2009, determined to make the most annual essay contest run by the Gravity Many physicists believe of his first sabbatical from teaching, Mark Research Foundation in Wellesley, Massachu- Van Raamsdonk decided to tackle one of setts. Not only did he win first prize, but he also that entanglement is Ithe deepest mysteries in physics: the relation- got to savour a particularly satisfying irony: the the essence of quantum ship between quantum mechanics and gravity. honour included guaranteed publication in After a year of work and consultation with col- General Relativity and Gravitation. The journal PICTURES PARAMOUNT weirdness — and some now leagues, he submitted a paper on the topic to published the shorter essay1 in June 2010. suspect that it may also be the Journal of High Energy Physics. Still, the editors had good reason to be BROS. ENTERTAINMENT/ WARNER In April 2010, the journal sent him a rejec- cautious. A successful unification of quantum the essence of space-time. tion — with a referee’s report implying that mechanics and gravity has eluded physicists Van Raamsdonk, a physicist at the University of for nearly a century. Quantum mechanics gov- British Columbia in Vancouver, was a crackpot. erns the world of the small — the weird realm His next submission, to General Relativity in which an atom or particle can be in many BY RON COWEN and Gravitation, fared little better: the referee’s places at the same time, and can simultaneously report was scathing, and the journal’s editor spin both clockwise and anticlockwise. Gravity asked for a complete rewrite. -
COVID-19 Detection on IBM Quantum Computer with Classical-Quantum Transfer Learning
medRxiv preprint doi: https://doi.org/10.1101/2020.11.07.20227306; this version posted November 10, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Turk J Elec Eng & Comp Sci () : { © TUB¨ ITAK_ doi:10.3906/elk- COVID-19 detection on IBM quantum computer with classical-quantum transfer learning Erdi ACAR1*, Ihsan_ YILMAZ2 1Department of Computer Engineering, Institute of Science, C¸anakkale Onsekiz Mart University, C¸anakkale, Turkey 2Department of Computer Engineering, Faculty of Engineering, C¸anakkale Onsekiz Mart University, C¸anakkale, Turkey Received: .201 Accepted/Published Online: .201 Final Version: ..201 Abstract: Diagnose the infected patient as soon as possible in the coronavirus 2019 (COVID-19) outbreak which is declared as a pandemic by the world health organization (WHO) is extremely important. Experts recommend CT imaging as a diagnostic tool because of the weak points of the nucleic acid amplification test (NAAT). In this study, the detection of COVID-19 from CT images, which give the most accurate response in a short time, was investigated in the classical computer and firstly in quantum computers. Using the quantum transfer learning method, we experimentally perform COVID-19 detection in different quantum real processors (IBMQx2, IBMQ-London and IBMQ-Rome) of IBM, as well as in different simulators (Pennylane, Qiskit-Aer and Cirq). By using a small number of data sets such as 126 COVID-19 and 100 Normal CT images, we obtained a positive or negative classification of COVID-19 with 90% success in classical computers, while we achieved a high success rate of 94-100% in quantum computers. -
A Theoretical Study of Quantum Memories in Ensemble-Based Media
A theoretical study of quantum memories in ensemble-based media Karl Bruno Surmacz St. Hugh's College, Oxford A thesis submitted to the Mathematical and Physical Sciences Division for the degree of Doctor of Philosophy in the University of Oxford Michaelmas Term, 2007 Atomic and Laser Physics, University of Oxford i A theoretical study of quantum memories in ensemble-based media Karl Bruno Surmacz, St. Hugh's College, Oxford Michaelmas Term 2007 Abstract The transfer of information from flying qubits to stationary qubits is a fundamental component of many quantum information processing and quantum communication schemes. The use of photons, which provide a fast and robust platform for encoding qubits, in such schemes relies on a quantum memory in which to store the photons, and retrieve them on-demand. Such a memory can consist of either a single absorber, or an ensemble of absorbers, with a ¤-type level structure, as well as other control ¯elds that a®ect the transfer of the quantum signal ¯eld to a material storage state. Ensembles have the advantage that the coupling of the signal ¯eld to the medium scales with the square root of the number of absorbers. In this thesis we theoretically study the use of ensembles of absorbers for a quantum memory. We characterize a general quantum memory in terms of its interaction with the signal and control ¯elds, and propose a ¯gure of merit that measures how well such a memory preserves entanglement. We derive an analytical expression for the entanglement ¯delity in terms of fluctuations in the stochastic Hamiltonian parameters, and show how this ¯gure could be measured experimentally. -
Quantum Inductive Learning and Quantum Logic Synthesis
Portland State University PDXScholar Dissertations and Theses Dissertations and Theses 2009 Quantum Inductive Learning and Quantum Logic Synthesis Martin Lukac Portland State University Follow this and additional works at: https://pdxscholar.library.pdx.edu/open_access_etds Part of the Electrical and Computer Engineering Commons Let us know how access to this document benefits ou.y Recommended Citation Lukac, Martin, "Quantum Inductive Learning and Quantum Logic Synthesis" (2009). Dissertations and Theses. Paper 2319. https://doi.org/10.15760/etd.2316 This Dissertation is brought to you for free and open access. It has been accepted for inclusion in Dissertations and Theses by an authorized administrator of PDXScholar. For more information, please contact [email protected]. QUANTUM INDUCTIVE LEARNING AND QUANTUM LOGIC SYNTHESIS by MARTIN LUKAC A dissertation submitted in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY in ELECTRICAL AND COMPUTER ENGINEERING. Portland State University 2009 DISSERTATION APPROVAL The abstract and dissertation of Martin Lukac for the Doctor of Philosophy in Electrical and Computer Engineering were presented January 9, 2009, and accepted by the dissertation committee and the doctoral program. COMMITTEE APPROVALS: Irek Perkowski, Chair GarrisoH-Xireenwood -George ^Lendaris 5artM ?teven Bleiler Representative of the Office of Graduate Studies DOCTORAL PROGRAM APPROVAL: Malgorza /ska-Jeske7~Director Electrical Computer Engineering Ph.D. Program ABSTRACT An abstract of the dissertation of Martin Lukac for the Doctor of Philosophy in Electrical and Computer Engineering presented January 9, 2009. Title: Quantum Inductive Learning and Quantum Logic Synhesis Since Quantum Computer is almost realizable on large scale and Quantum Technology is one of the main solutions to the Moore Limit, Quantum Logic Synthesis (QLS) has become a required theory and tool for designing Quantum Logic Circuits. -
Nearest Centroid Classification on a Trapped Ion Quantum Computer
www.nature.com/npjqi ARTICLE OPEN Nearest centroid classification on a trapped ion quantum computer ✉ Sonika Johri1 , Shantanu Debnath1, Avinash Mocherla2,3,4, Alexandros SINGK2,3,5, Anupam Prakash2,3, Jungsang Kim1 and Iordanis Kerenidis2,3,6 Quantum machine learning has seen considerable theoretical and practical developments in recent years and has become a promising area for finding real world applications of quantum computers. In pursuit of this goal, here we combine state-of-the-art algorithms and quantum hardware to provide an experimental demonstration of a quantum machine learning application with provable guarantees for its performance and efficiency. In particular, we design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations, and experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data. npj Quantum Information (2021) 7:122 ; https://doi.org/10.1038/s41534-021-00456-5 INTRODUCTION Thus, one might hope that noisy quantum computers are 1234567890():,; Quantum technologies promise to revolutionize the future of inherently better suited for machine learning computations than information and communication, in the form of quantum for other types of problems that need precise computations like computing devices able to communicate and process massive factoring or search problems. amounts of data both efficiently and securely using quantum However, there are significant challenges to be overcome to resources. Tremendous progress is continuously being made both make QML practical. -
Operation-Induced Decoherence by Nonrelativistic Scattering from a Quantum Memory
INSTITUTE OF PHYSICS PUBLISHING JOURNAL OF PHYSICS A: MATHEMATICAL AND GENERAL J. Phys. A: Math. Gen. 39 (2006) 11567–11581 doi:10.1088/0305-4470/39/37/015 Operation-induced decoherence by nonrelativistic scattering from a quantum memory D Margetis1 and J M Myers2 1 Department of Mathematics, and Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, USA 2 Division of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA E-mail: [email protected] and [email protected] Received 16 June 2006, in final form 7 August 2006 Published 29 August 2006 Online at stacks.iop.org/JPhysA/39/11567 Abstract Quantum computing involves transforming the state of a quantum memory. We view this operation as performed by transmitting nonrelativistic (massive) particles that scatter from the memory. By using a system of (1+1)- dimensional, coupled Schrodinger¨ equations with point interaction and narrow- band incoming pulse wavefunctions, we show how the outgoing pulse becomes entangled with a two-state memory. This effect necessarily induces decoherence, i.e., deviations of the memory content from a pure state. We describe incoming pulses that minimize this decoherence effect under a constraint on the duration of their interaction with the memory. PACS numbers: 03.65.−w, 03.67.−a, 03.65.Nk, 03.65.Yz, 03.67.Lx 1. Introduction Quantum computing [1–4] relies on a sequence of operations, each of which transforms a pure quantum state to, ideally, another pure state. These states describe a physical system, usually called memory. A central problem in quantum computing is decoherence, in which the pure state degrades to a mixed state, with deleterious effects for the computation. -
A Live Alternative to Quantum Spooks
International Journal of Quantum Foundations 6 (2020) 1-8 Original Paper A live alternative to quantum spooks Huw Price1;∗ and Ken Wharton2 1. Trinity College, Cambridge CB2 1TQ, UK 2. Department of Physics and Astronomy, San José State University, San José, CA 95192-0106, USA * Author to whom correspondence should be addressed; E-mail:[email protected] Received: 14 December 2019 / Accepted: 21 December 2019 / Published: 31 December 2019 Abstract: Quantum weirdness has been in the news recently, thanks to an ingenious new experiment by a team led by Roland Hanson, at the Delft University of Technology. Much of the coverage presents the experiment as good (even conclusive) news for spooky action-at-a-distance, and bad news for local realism. We point out that this interpretation ignores an alternative, namely that the quantum world is retrocausal. We conjecture that this loophole is missed because it is confused for superdeterminism on one side, or action-at-a-distance itself on the other. We explain why it is different from these options, and why it has clear advantages, in both cases. Keywords: Quantum Entanglement; Retrocausality; Superdeterminism Quantum weirdness has been getting a lot of attention recently, thanks to a clever new experiment by a team led by Roland Hanson, at the Delft University of Technology.[1] Much of the coverage has presented the experiment as good news for spooky action-at-a-distance, and bad news for Einstein: “The most rigorous test of quantum theory ever carried out has confirmed that the ‘spooky action-at-a-distance’ that [Einstein] famously hated . -
Experimental Kernel-Based Quantum Machine Learning in Finite Feature
www.nature.com/scientificreports OPEN Experimental kernel‑based quantum machine learning in fnite feature space Karol Bartkiewicz1,2*, Clemens Gneiting3, Antonín Černoch2*, Kateřina Jiráková2, Karel Lemr2* & Franco Nori3,4 We implement an all‑optical setup demonstrating kernel‑based quantum machine learning for two‑ dimensional classifcation problems. In this hybrid approach, kernel evaluations are outsourced to projective measurements on suitably designed quantum states encoding the training data, while the model training is processed on a classical computer. Our two-photon proposal encodes data points in a discrete, eight-dimensional feature Hilbert space. In order to maximize the application range of the deployable kernels, we optimize feature maps towards the resulting kernels’ ability to separate points, i.e., their “resolution,” under the constraint of fnite, fxed Hilbert space dimension. Implementing these kernels, our setup delivers viable decision boundaries for standard nonlinear supervised classifcation tasks in feature space. We demonstrate such kernel-based quantum machine learning using specialized multiphoton quantum optical circuits. The deployed kernel exhibits exponentially better scaling in the required number of qubits than a direct generalization of kernels described in the literature. Many contemporary computational problems (like drug design, trafc control, logistics, automatic driving, stock market analysis, automatic medical examination, material engineering, and others) routinely require optimiza- tion over huge amounts of data1. While these highly demanding problems can ofen be approached by suitable machine learning (ML) algorithms, in many relevant cases the underlying calculations would last prohibitively long. Quantum ML (QML) comes with the promise to run these computations more efciently (in some cases exponentially faster) by complementing ML algorithms with quantum resources. -
Quantum Entanglement and Bell's Inequality
Quantum Entanglement and Bell’s Inequality Christopher Marsh, Graham Jensen, and Samantha To University of Rochester, Rochester, NY Abstract – Christopher Marsh: Entanglement is a phenomenon where two particles are linked by some sort of characteristic. A particle such as an electron can be entangled by its spin. Photons can be entangled through its polarization. The aim of this lab is to generate and detect photon entanglement. This was accomplished by subjecting an incident beam to spontaneous parametric down conversion, a process where one photon produces two daughter polarization entangled photons. Entangled photons were sent to polarizers which were placed in front of two avalanche photodiodes; by changing the angles of these polarizers we observed how the orientation of the polarizers was linked to the number of photons coincident on the photodiodes. We dabbled into how aligning and misaligning a phase correcting quartz plate affected data. We also set the polarizers to specific angles to have the maximum S value for the Clauser-Horne-Shimony-Holt inequality. This inequality states that S is no greater than 2 for a system obeying classical physics. In our experiment a S value of 2.5 ± 0.1 was calculated and therefore in violation of classical mechanics. 1. Introduction – Graham Jensen We report on an effort to verify quantum nonlocality through a violation of Bell’s inequality using polarization-entangled photons. When light is directed through a type 1 Beta Barium Borate (BBO) crystal, a small fraction of the incident photons (on the order of ) undergo spontaneous parametric downconversion. In spontaneous parametric downconversion, a single pump photon is split into two new photons called the signal and idler photons.